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Testing Racial Bias in Police Officers Using a Shooting Simulator

Undergraduate #363
Discipline: Social, Behavioral, and Economic Sciences
Subcategory: Social Sciences/Psychology/Economics

Matthew Bolton - Prairie View A&M University


Police officers must often make split-second decisions about whether to shoot a suspect. Researchers have been interested in whether this decision is biased by information such as the race of a suspect. In preceding studies, results show that civilian participants show racial bias in their decisions to shoot Black and White targets. Unarmed Black targets were shot by civilians more frequently than unarmed White targets in video game-like tasks using button responses to still images on a computer screen. In an attempt to improve this task, we addressed these concerns by having police officers from a major Midwest municipal police force fire a modified handgun in response to life-sized videos of armed and unarmed targets. The findings of this current study demonstrate that officers shot armed Black targets quicker than armed White targets, but this effect was not significant. Results also found that police officers shot unarmed Black targets and unarmed White targets at similar rates, showing no race bias, in contrast to untrained individuals from previous studies. These findings suggest that officers can better focus on relevant information such as weapon identification rather than irrelevant information (race) in experimental shooting simulation task.

Funder Acknowledgement(s): Michigan State University

Faculty Advisor: Joesph Cesario, josephcesario@gmail.com

Role: As a Summer Research Opportunities Program Intern at Michigan State University I researched racial bias in decisions to shoot armed and unarmed targets. My involvement included literature review, creating and editing video for the simulator task, preparing the simulators parts for participants, scheduling participants, running participants through the experiment, and data analysis.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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